Dust is an enterprise-grade AI agent platform that helps organizations deploy, orchestrate, and govern custom AI agents connected to internal data and tools. Teams can build agents without code, integrate knowledge from systems like Slack and Notion, and automate workflows across departments.
The platform emphasizes security, compliance, and flexibility, making it suitable for both startups and large enterprises.
| Plan | Price | Featured |
|---|---|---|
| Pro | €29/user/month (Billed Monthly, excl. tax) | Advanced models (GPT-4, Claude), Unlimited messages (fair use), Native integrations (Slack, Zendesk, Chrome) |
| Enterprise | Custom pay-per-use pricing (100+ users) | SSO & SCIM provisioning, Advanced security & audit logs, Priority support & dedicated account management |
Price details: https://dust.tt/home/pricing
Competitor |
Pros |
|---|---|
| LangChain | Dust is easier to use for non-developers, offering a visual, no-code interface instead of code-heavy workflows. Pricing is predictable per seat rather than infrastructure-based, and teams can deploy agents faster without maintaining complex pipelines. |
| ChatGPT Enterprise | Dust provides deeper customization through task-specific agents and internal tool integrations. It is more flexible for workflow automation, while pricing per user is often clearer and more accessible for smaller teams. |
| AirOps | Compared to AirOps’ content-focused workflows, Dust supports broader enterprise use cases like IT, legal, and analytics. Its interface is more general-purpose, making it easier for cross-functional teams to adopt. |
| Relevance AI | Dust emphasizes security and compliance more strongly, which appeals to regulated organizations. It also offers tighter native integrations with common enterprise tools, reducing setup complexity. |
| Zapier AI | Dust focuses on intelligent agents rather than simple automation, enabling richer reasoning and context awareness. It is better suited for knowledge-heavy workflows beyond trigger-based task automation. |
Competitor |
Cons |
|---|---|
| LangChain | Developers who want full programmatic control may find Dust less flexible than LangChain. Advanced custom logic and experimental agent architectures are easier to implement directly in code-first frameworks. |
| ChatGPT Enterprise | ChatGPT Enterprise can be simpler for organizations that only need conversational AI. Dust introduces more configuration and concepts, which may feel heavier for teams seeking a minimal setup. |
| AirOps | AirOps may outperform Dust for highly specialized content marketing workflows. Dust’s broader scope can mean slightly more onboarding time for teams focused only on content generation. |
| Relevance AI | Relevance AI offers stronger out-of-the-box analytics and dataset tooling. Dust prioritizes agent workflows, which may require additional configuration for advanced data science use cases. |
| Zapier AI | Zapier AI is often easier for quick automations across thousands of apps. Dust requires more upfront setup but delivers more intelligence, which may be unnecessary for simple workflows. |
